An Investigation on Effect of Depth of Cut, Feed Rate and Tool Nose Radius on Induced Vibration and Surface Roughness during Hard Turning of 41Cr4 Alloy Steel Using Response Surface Methodology

Christopher Okechukwu Izelu,Samuel Chike Eze

doi:10.18052/www.scipress.com/ijet.7.32

Abstract

This paper describes an aspect of a set of turning experiments performed in attempt to model, predict and optimize the machining induced vibration and surface roughness as functions of the machining, tool and work-piece variables during hard turning of 41Cr4 alloy special steel, with standard cutting tool, on a conventional lathe. Amongst others, the input variables of interest include the depth of cut, feed rate and tool nose radius. The response surface methodology, based on central composite design of experiment, was adopted, with analysis performed in Design Expert 9 software environment. Quadratic regression models were suggested, and proved significant by an analysis of variance, for the machining induced vibration of the cutting tool and surface roughness of the work-piece. They also have capability of being used for prediction within limits. Analysis of variance also showed the depth of cut, feed rate and tool nose radius have significant effect on the machining induced vibration and surface roughness. Whereas the depth of cut has dominant effect on the machining induced vibration, the tool nose radius has dominant effect on the surface roughness. The optimum setting of the depth of cut of 1.33095 mm, feed rate of 0.168695 mm/rev, and the tool nose radius of 1.71718 mm is required to minimize the machining induced vibration at 0.08 mm/s2 and surface roughness at 6.056 μmm with a desirability of 0.830.

Highlights

A company’s survival in global competition essentially depends on the quality of its products and services
In order to minimize the number of runs or trials, optimize values of parameters, assess experimental error, make qualitative estimation of parameters, and to make inference regarding the effect of parameters on the characteristics of the process (Aggarwal and Singh, 2005), it is essential to adopt any of the techniques of design of experiment for the turning experiment
Within limits of the selected factors, the developed quadratic regression models can be used for accurate prediction of the selected process and quality characteristics

Summary

Introduction

A company’s survival in global competition essentially depends on the quality of its products and services. As an obligation for processes to work properly in time and at all times, it is of necessity that machined component manufacturers adopt better approaches to ensure that high quality products and services are produced. This drive for quality, and sometimes performance, has motivated efforts leading to the search for optimization techniques, and a shift from the use of traditional to non-traditional techniques, such as reviewed in Aggarwal and Singh [1]), and in Kumar and Uppal [2]. Relevant to this work, are the works of Ozcakar and Kasapoglu [3], Abhang and Hameedullah [4], Sahoo [5], Abhang and Hameedullah [6], Sastry and Devi [7], Srinivasan et al [8], Ramudu and Sastry [9], Aruna and Dhanalaksmi [10], Chomsamutr and Jongprasithporn [11], Abhang and Hameedullah [12], Manu et al [13], Makadia and Nanavati [14], Kannan et al [15], Phate and Tatwawadi [16], Bhuiyan and Ahmed [17], Manohar et al [18], SciPress applies the CC-BY 4.0 license to works we publish: https://creativecommons.org/licenses/by/4.0/

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